September 2023 - talks - lists.cs.princeton.edu

ECE Korhammer Seminar: Meir Feder, Tel Aviv University (Wed., Sept. 20, 2023 @ 4:30pm, B205)
by Emily C. Lawrence 11 Sep '23

11 Sep '23

ECE KORHAMMER SEMINAR SERIES Speaker: Meir Feder, Tel Aviv University Title: Multiple and Hierarchical Universality Day: September 20, 2023 Time: 4:30pm Location: B205 Engineering Quadrangle Hosts: H. Vincent Poor and Andrea Goldsmith Abstract: Universal coding, prediction and learning usually consider the case where the data generating mechanism is unknown or non-existent, and the goal of the universal scheme is to compete with the best hypothesis from a given hypothesis class, either on the average or in a worst-case scenario. Multiple universality considers the case where the hypothesis class is also unknown: there are several hypothesis classes with possibly different complexities. In hierarchical universality, the simpler classes are nested within more complex classes. The main challenge is to correctly define the universality criterion so that the extra “regret” for not knowing the class is monitored. We propose several possible definitions and derive their min-max optimal solutions. Interestingly, the proposed approach can be used to obtain Elias codes for universal representation of the integers. We further utilize this approach for variable-memory Markov models (unifilar models), presenting a new interpretation for the bound over the regret of the celebrated context-tree weighting algorithm, and suggest a multiple universality approach for general linear models, including linear regression, and logistic regression and Perceptrons. Finally, we conjecture that multiple universality, with its non-uniform convergence and regret bounds, can be applied to explain and design learning schemes in general “over-parameterized” model classes such as deep neural networks, transformers and so on. Joint work with Yaniv Fogel. Bio: Meir Feder received the B.Sc and M.Sc degrees in Electrical Engineering in 1980 and 1984 from Tel-Aviv University and the Sc.D degree in Electrical Engineering and Ocean Engineering in 1987 from the Massachusetts Institute of Technology (MIT) and the Woods Hole Oceanographic Institution (WHOI). After being a Research Associate and a Lecturer in MIT, he joined the School of Electrical Engineering, Tel-Aviv University, where he is now the Jokel Chaired Professor and the head of the newly established Tel-Aviv university center for Artificial intelligence and Data science (TAD). He is also a Visiting Professor with the Department of EECS, MIT. Parallel to his academic career, he is closely involved with the high-tech industry. He founded 5 companies, among them Peach Networks that developed an interactive TV solution (Acq: MSFT) and Amimon that provided the highest quality, robust, no latency wireless high-definition A/V connectivity (Acq:LON.VTC). Recently, with his renewed interest in machine learning and AI, he cofounded Run:ai, a virtualization, orchestration, and acceleration platform for AI infrastructure. He is also an active angel investor and serves on the board/advisory board of several US and Israeli companies. Prof. Feder received several academic and professional awards including the IEEE Information Theory Society best paper award for his work on universal prediction, the “creative thinking” award of the Israeli Defense Forces and the Research Prize of the Israeli Electronic Industry, awarded by the President of Israel. For the development of Amimon’s chip-set, that uses a unique MIMO implementation of joint source-channel coding for wireless video transmission, he received the 2020 Scientific and Engineering Award of the Academy of Motion Picture Arts and Sciences (Oscar). This seminar is supported with funds from the Korhammer Lecture Series.

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New Seminar Series - ML & Physics
by Elizabeth Wang 06 Sep '23

06 Sep '23

Greetings, Professor Boris Hanin from Operations Research & Financial Engineering would like to share the following seminar series with COS students, postdocs, and faculty. If you have any questions, please contact Boris at [ mailto:bhanin@princeton.edu | bhanin(a)princeton.edu ] . >From Boris: Dear Colleagues, We wanted to bring to your attention a new seminar series, Machine Learning in Physics, focused on interdisciplinary work at the intersection of physics and ML. Here are the details: What ? A seminar series highlighting research on both physics-inspired approaches to understanding ML and the use of ML for physics applications When/Where ? Wednesdays 4:30 - 5:30pm in Jadwin Hall, Room A10 with light refreshments after the presentation. Who ? The speakers for Fall 2023 are as follows: * Sept. 27 : Shirley Ho (Flatiron) * Oct. 4 : Juan Felipe Carrasquilla Alvarez (Vector) * Nov. 1: Javier Duarte (UCSD) * Nov. 15 : Phiala Shananan (MIT) * Nov 29 : Andy Leifer (Princeton) For more details — and to sign up for the seminar mailing list — please see the seminar [ https://ml.physics.princeton.edu/ | website ] . Best, Boris Hanin, Gage DeZoort, Dan Marlow, Mariangela Lisanti, Peter Elmer (seminar co-organizers)

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“Tony” Runzhe Yang will present his FPO "From mind to machine: neural circuits, learning algorithms, and beyond." on Tuesday, September 26, 2023 at 12:00 PM in CS 105 and Zoom.
by CS Grad Department 06 Sep '23

06 Sep '23

“Tony” Runzhe Yang will present his FPO "From mind to machine: neural circuits, learning algorithms, and beyond." on Tuesday, September 26, 2023 at 12:00 PM in CS 105 and Zoom. Location: Zoom Link: [ https://princeton.zoom.us/j/91084214303 | https://princeton.zoom.us/j/91084214303 ] The members of Runzhe’s committee are as follows: Examiners: H. Sebastian Seung (Adviser), Karthik Narasimhan (Adviser), Mala Murthy Readers: Ryan Adams, Dmitri Chklovskii (Flatiron Institute; NYU) A copy of his thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: This thesis explores diverse topics within computational neuroscience and machine learning. The work begins by examining the organization of biological neuronal circuits re- constructed by electron microscopy. First, our study of neural connectivity patterns in the mouse primary visual cortex illustrates the necessity for refined understanding of the non- random features of cortical connections, challenging conventional perspectives. Second, in the larval zebrafish hindbrain, our novel discovery highlights overrepresented three-cycles of neuron, an observation unprecedented in electron microscopy-reconstructed neuronal wiring diagrams. Additionally, I present an exhaustive compilation of motif statistics and network characteristics for the complete adult Drosophila brain. These efforts collectively enrich our understanding of the intricate wiring diagram of neurons, offering new insights into the organizational principles of biological brains. In the second part of the thesis, I introduce three distinct machine learning algorithms. The first algorithm, a biologically plausible unsupervised learning algorithm, is implemented within artificial neural networks using Hebbian feedforward and anti-Hebbian lateral connections. The theoretical discourse explores the duality and convergence of the learning process, connecting with the generalized concept of the “correlation game” principle. The second algorithm presents a novel multi-objective reinforcement learning approach, adept at managing real-world scenarios where multiple potentially conflicting criteria must be optimized without predefined importance weighting. This innovation allows the trained neural network model to generate policies that align optimally with user- specified preferences across the entire space of preference. The third algorithm employs a cognitive science-inspired learning principle for dialog systems. The designed system engages in negotiation with others, skillfully inferring the intent of the other party and predicting how its responses may influence the opponent’s mental state. Collectively, these contributions shed light on the complexities of neural circuit organization and offer new methodologies in machine learning. By examining intelligence from both biological and computational perspectives, the thesis presents insights and reference points for future research, contributing to our growing understanding of intelligence.

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